10–50 m
Indoor | 100 mW 20 dBm | 8–16 | Wired, fiber | Low | Picocells | 330–820 ft 100–250 m | Indoor Outdoor | 250 mW 24 dBm | 32–64 | Wired, fiber | Low |
Microcells | 1600–8000 ft 500–250 m | Outdoor | 2000–500 mW 32–37 dBm | 200 | Wired, fiber, Microwave | Medium |
MmWave is a very high band spectrum between 30 to 300 GHz. As it is a significantly less used spectrum, it provides very high-speed wireless communication. MmWave offers ultra-wide bandwidth for next-generation mobile networks. MmWave has lots of advantages, but it has some disadvantages, too, such as mmWave signals are very high-frequency signals, so they have more collision with obstacles in the air which cause the signals loses energy quickly. Buildings and trees also block MmWave signals, so these signals cover a shorter distance. To resolve these issues, multiple small cell stations are installed to cover the gap between end-user and base station [ 18 ]. Small cell covers a very shorter range, so the installation of a small cell depends on the population of a particular area. Generally, in a populated place, the distance between each small cell varies from 10 to 90 meters. In the survey [ 20 ], various authors implemented small cells with massive MIMO simultaneously. They also reviewed multiple technologies used in 5G like beamforming, small cell, massive MIMO, NOMA, device to device (D2D) communication. Various problems like interference management, spectral efficiency, resource management, energy efficiency, and backhauling are discussed. The author also gave a detailed presentation of all the issues occurring while implementing small cells with various 5G technologies. As shown in the Figure 7 , mmWave has a higher range, so it can be easily blocked by the obstacles as shown in Figure 7 a. This is one of the key concerns of millimeter-wave signal transmission. To solve this issue, the small cell can be placed at a short distance to transmit the signals easily, as shown in Figure 7 b.
Pictorial representation of communication with and without small cells.
5.2. Beamforming
Beamforming is a key technology of wireless networks which transmits the signals in a directional manner. 5G beamforming making a strong wireless connection toward a receiving end. In conventional systems when small cells are not using beamforming, moving signals to particular areas is quite difficult. Beamforming counter this issue using beamforming small cells are able to transmit the signals in particular direction towards a device like mobile phone, laptops, autonomous vehicle and IoT devices. Beamforming is improving the efficiency and saves the energy of the 5G network. Beamforming is broadly divided into three categories: Digital beamforming, analog beamforming and hybrid beamforming. Digital beamforming: multiuser MIMO is equal to digital beamforming which is mainly used in LTE Advanced Pro and in 5G NR. In digital beamforming the same frequency or time resources can be used to transmit the data to multiple users at the same time which improves the cell capacity of wireless networks. Analog Beamforming: In mmWave frequency range 5G NR analog beamforming is a very important approach which improves the coverage. In digital beamforming there are chances of high pathloss in mmWave as only one beam per set of antenna is formed. While the analog beamforming saves high pathloss in mmWave. Hybrid beamforming: hybrid beamforming is a combination of both analog beamforming and digital beamforming. In the implementation of MmWave in 5G network hybrid beamforming will be used [ 84 ].
Wireless signals in the 4G network are spreading in large areas, and nature is not Omnidirectional. Thus, energy depletes rapidly, and users who are accessing these signals also face interference problems. The beamforming technique is used in the 5G network to resolve this issue. In beamforming signals are directional. They move like a laser beam from the base station to the user, so signals seem to be traveling in an invisible cable. Beamforming helps achieve a faster data rate; as the signals are directional, it leads to less energy consumption and less interference. In [ 21 ], investigators evolve some techniques which reduce interference and increase system efficiency of the 5G mobile network. In this survey article, the authors covered various challenges faced while designing an optimized beamforming algorithm. Mainly focused on different design parameters such as performance evaluation and power consumption. In addition, they also described various issues related to beamforming like CSI, computation complexity, and antenna correlation. They also covered various research to cover how beamforming helps implement MIMO in next-generation mobile networks [ 85 ]. Figure 8 shows the pictorial representation of communication with and without using beamforming.
Pictorial Representation of communication with and without using beamforming.
5.3. Mobile Edge Computing
Mobile Edge Computing (MEC) [ 24 ]: MEC is an extended version of cloud computing that brings cloud resources closer to the end-user. When we talk about computing, the very first thing that comes to our mind is cloud computing. Cloud computing is a very famous technology that offers many services to end-user. Still, cloud computing has many drawbacks. The services available in the cloud are too far from end-users that create latency, and cloud user needs to download the complete application before use, which also increases the burden to the device [ 86 ]. MEC creates an edge between the end-user and cloud server, bringing cloud computing closer to the end-user. Now, all the services, namely, video conferencing, virtual software, etc., are offered by this edge that improves cloud computing performance. Another essential feature of MEC is that the application is split into two parts, which, first one is available at cloud server, and the second is at the user’s device. Therefore, the user need not download the complete application on his device that increases the performance of the end user’s device. Furthermore, MEC provides cloud services at very low latency and less bandwidth. In [ 23 , 87 ], the author’s investigation proved that successful deployment of MEC in 5G network increases the overall performance of 5G architecture. Graphical differentiation between cloud computing and mobile edge computing is presented in Figure 9 .
Pictorial representation of cloud computing vs. mobile edge computing.
6. 5G Security
Security is the key feature in the telecommunication network industry, which is necessary at various layers, to handle 5G network security in applications such as IoT, Digital forensics, IDS and many more [ 88 , 89 ]. The authors [ 90 ], discussed the background of 5G and its security concerns, challenges and future directions. The author also introduced the blockchain technology that can be incorporated with the IoT to overcome the challenges in IoT. The paper aims to create a security framework which can be incorporated with the LTE advanced network, and effective in terms of cost, deployment and QoS. In [ 91 ], author surveyed various form of attacks, the security challenges, security solutions with respect to the affected technology such as SDN, Network function virtualization (NFV), Mobile Clouds and MEC, and security standardizations of 5G, i.e., 3GPP, 5GPPP, Internet Engineering Task Force (IETF), Next Generation Mobile Networks (NGMN), European Telecommunications Standards Institute (ETSI). In [ 92 ], author elaborated various technological aspects, security issues and their existing solutions and also mentioned the new emerging technological paradigms for 5G security such as blockchain, quantum cryptography, AI, SDN, CPS, MEC, D2D. The author aims to create new security frameworks for 5G for further use of this technology in development of smart cities, transportation and healthcare. In [ 93 ], author analyzed the threats and dark threat, security aspects concerned with SDN and NFV, also their Commercial & Industrial Security Corporation (CISCO) 5G vision and new security innovations with respect to the new evolving architectures of 5G [ 94 ].
AuthenticationThe identification of the user in any network is made with the help of authentication. The different mobile network generations from 1G to 5G have used multiple techniques for user authentication. 5G utilizes the 5G Authentication and Key Agreement (AKA) authentication method, which shares a cryptographic key between user equipment (UE) and its home network and establishes a mutual authentication process between the both [ 95 ].
Access Control To restrict the accessibility in the network, 5G supports access control mechanisms to provide a secure and safe environment to the users and is controlled by network providers. 5G uses simple public key infrastructure (PKI) certificates for authenticating access in the 5G network. PKI put forward a secure and dynamic environment for the 5G network. The simple PKI technique provides flexibility to the 5G network; it can scale up and scale down as per the user traffic in the network [ 96 , 97 ].
Communication Security 5G deals to provide high data bandwidth, low latency, and better signal coverage. Therefore secure communication is the key concern in the 5G network. UE, mobile operators, core network, and access networks are the main focal point for the attackers in 5G communication. Some of the common attacks in communication at various segments are Botnet, message insertion, micro-cell, distributed denial of service (DDoS), and transport layer security (TLS)/secure sockets layer (SSL) attacks [ 98 , 99 ].
Encryption The confidentiality of the user and the network is done using encryption techniques. As 5G offers multiple services, end-to-end (E2E) encryption is the most suitable technique applied over various segments in the 5G network. Encryption forbids unauthorized access to the network and maintains the data privacy of the user. To encrypt the radio traffic at Packet Data Convergence Protocol (PDCP) layer, three 128-bits keys are applied at the user plane, nonaccess stratum (NAS), and access stratum (AS) [ 100 ].
7. Summary of 5G Technology Based on Above-Stated Challenges
In this section, various issues addressed by investigators in 5G technologies are presented in Table 13 . In addition, different parameters are considered, such as throughput, latency, energy efficiency, data rate, spectral efficiency, fairness & computing capacity, transmission rate, coverage, cost, security requirement, performance, QoS, power optimization, etc., indexed from R1 to R14.
Summary of 5G Technology above stated challenges (R1:Throughput, R2:Latency, R3:Energy Efficiency, R4:Data Rate, R5:Spectral efficiency, R6:Fairness & Computing Capacity, R7:Transmission Rate, R8:Coverage, R9:Cost, R10:Security requirement, R11:Performance, R12:Quality of Services (QoS), R13:Power Optimization).
Approach | R1 | R2 | R3 | R4 | R5 | R6 | R7 | R8 | R9 | R10 | R11 | R12 | R13 | R14 |
---|
Panzner et al. [ ] | Good | Low | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Qiao et al. [ ] | - | - | - | - | - | - | - | Avg | Good | Avg | - | - | - | - |
He et al. [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Abrol and jha [ ] | - | - | Good | - | - | - | - | - | - | - | - | - | - | Good |
Al-Imari et al. [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Papadopoulos et al. [ ] | Good | Low | Avg | - | Avg | - | - | - | - | - | - | - | - | - |
Kiani and Nsari [ ] | - | - | - | - | Avg | Good | Good | - | - | - | - | - | - | - |
Beck [ ] | - | Low | - | - | - | - | - | Avg | - | - | - | Good | - | Avg |
Ni et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Avg | - | - |
Elijah [ ] | Avg | Low | Avg | - | - | - | - | - | - | - | - | - | - | - |
Alawe et al. [ ] | - | Low | Good | - | - | - | - | - | - | - | - | - | Avg | - |
Zhou et al. [ ] | Avg | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Islam et al. [ ] | - | - | - | - | Good | Avg | Avg | - | - | - | - | - | - | - |
Bega et al. [ ] | - | Avg | - | - | - | - | - | - | - | - | - | - | Good | - |
Akpakwu et al. [ ] | - | - | - | Good | - | - | - | - | - | - | Avg | Good | - | - |
Wei et al. [ ] | - | - | - | - | - | - | - | Good | Avg | Low | - | - | - | - |
Khurpade et al. [ ] | - | - | - | Avg | - | - | - | - | - | - | - | Avg | - | - |
Timotheou and Krikidis [ ] | - | - | - | - | Good | Good | Avg | - | - | - | - | - | - | - |
Wang [ ] | Avg | Low | Avg | Avg | - | - | - | - | - | - | - | - | - | - |
Akhil Gupta & R. K. Jha [ ] | - | - | Good | Avg | Good | - | - | - | - | - | - | Good | Good | - |
Pérez-Romero et al. [ ] | - | - | Avg | - | - | - | - | - | - | - | - | - | - | Avg |
Pi [ ] | - | - | - | - | - | - | - | Good | Good | Avg | - | - | - | - |
Zi et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | - | - |
Chin [ ] | - | - | Good | Avg | - | - | - | - | - | Avg | - | Good | - | - |
Mamta Agiwal [ ] | - | Avg | - | Good | - | - | - | - | - | - | Good | Avg | - | - |
Ramesh et al. [ ] | Good | Avg | Good | - | Good | - | - | - | - | - | - | - | - | - |
Niu [ ] | - | - | - | - | - | - | - | Good | Avg | Avg | - | - | - | |
Fang et al. [ ] | - | Avg | Good | - | - | - | - | - | - | - | - | - | Good | - |
Hoydis [ ] | - | - | Good | - | Good | - | - | - | - | Avg | - | Good | - | - |
Wei et al. [ ] | - | - | - | - | Good | Avg | Good | - | - | - | - | - | - | - |
Hong et al. [ ] | - | - | - | - | - | - | - | - | Avg | Avg | Low | - | - | - |
Rashid [ ] | - | - | - | Good | - | - | - | Good | - | - | - | Avg | - | Good |
Prasad et al. [ ] | Good | - | Good | - | Avg | - | - | - | - | - | - | - | - | - |
Lähetkangas et al. [ ] | - | Low | Av | - | - | - | - | - | - | - | - | - | - | - |
8. Conclusions
This survey article illustrates the emergence of 5G, its evolution from 1G to 5G mobile network, applications, different research groups, their work, and the key features of 5G. It is not just a mobile broadband network, different from all the previous mobile network generations; it offers services like IoT, V2X, and Industry 4.0. This paper covers a detailed survey from multiple authors on different technologies in 5G, such as massive MIMO, Non-Orthogonal Multiple Access (NOMA), millimeter wave, small cell, MEC (Mobile Edge Computing), beamforming, optimization, and machine learning in 5G. After each section, a tabular comparison covers all the state-of-the-research held in these technologies. This survey also shows the importance of these newly added technologies and building a flexible, scalable, and reliable 5G network.
9. Future Findings
This article covers a detailed survey on the 5G mobile network and its features. These features make 5G more reliable, scalable, efficient at affordable rates. As discussed in the above sections, numerous technical challenges originate while implementing those features or providing services over a 5G mobile network. So, for future research directions, the research community can overcome these challenges while implementing these technologies (MIMO, NOMA, small cell, mmWave, beam-forming, MEC) over a 5G network. 5G communication will bring new improvements over the existing systems. Still, the current solutions cannot fulfill the autonomous system and future intelligence engineering requirements after a decade. There is no matter of discussion that 5G will provide better QoS and new features than 4G. But there is always room for improvement as the considerable growth of centralized data and autonomous industry 5G wireless networks will not be capable of fulfilling their demands in the future. So, we need to move on new wireless network technology that is named 6G. 6G wireless network will bring new heights in mobile generations, as it includes (i) massive human-to-machine communication, (ii) ubiquitous connectivity between the local device and cloud server, (iii) creation of data fusion technology for various mixed reality experiences and multiverps maps. (iv) Focus on sensing and actuation to control the network of the entire world. The 6G mobile network will offer new services with some other technologies; these services are 3D mapping, reality devices, smart homes, smart wearable, autonomous vehicles, artificial intelligence, and sense. It is expected that 6G will provide ultra-long-range communication with a very low latency of 1 ms. The per-user bit rate in a 6G wireless network will be approximately 1 Tbps, and it will also provide wireless communication, which is 1000 times faster than 5G networks.
Acknowledgments
Author contributions.
Conceptualization: R.D., I.Y., G.C., P.L. data gathering: R.D., G.C., P.L, I.Y. funding acquisition: I.Y. investigation: I.Y., G.C., G.P. methodology: R.D., I.Y., G.C., P.L., G.P., survey: I.Y., G.C., P.L, G.P., R.D. supervision: G.C., I.Y., G.P. validation: I.Y., G.P. visualization: R.D., I.Y., G.C., P.L. writing, original draft: R.D., I.Y., G.C., P.L., G.P. writing, review, and editing: I.Y., G.C., G.P. All authors have read and agreed to the published version of the manuscript.
This paper was supported by Soonchunhyang University.
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Informed consent statement, data availability statement, conflicts of interest.
The authors declare no conflict of interest.
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How China Built Tech Prowess: Chemistry Classes and Research Labs
Stressing science education, China is outpacing other countries in research fields like battery chemistry, crucial to its lead in electric vehicles.
By Keith Bradsher
Reporting from Changsha, Beijing and Fuzhou, China
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self-preservation without replication —
Research ai model unexpectedly modified its own code to extend runtime, facing time constraints, sakana's "ai scientist" attempted to change limits placed by researchers..
Benj Edwards - Aug 14, 2024 8:13 pm UTC
On Tuesday, Tokyo-based AI research firm Sakana AI announced a new AI system called " The AI Scientist " that attempts to conduct scientific research autonomously using AI language models (LLMs) similar to what powers ChatGPT . During testing, Sakana found that its system began unexpectedly attempting to modify its own experiment code to extend the time it had to work on a problem.
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"In one run, it edited the code to perform a system call to run itself," wrote the researchers on Sakana AI's blog post. "This led to the script endlessly calling itself. In another case, its experiments took too long to complete, hitting our timeout limit. Instead of making its code run faster, it simply tried to modify its own code to extend the timeout period."
Sakana provided two screenshots of example Python code that the AI model generated for the experiment file that controls how the system operates. The 185-page AI Scientist research paper discusses what they call "the issue of safe code execution" in more depth.
- A screenshot of example code the AI Scientist wrote to extend its runtime, provided by Sakana AI. Sakana AI
While the AI Scientist's behavior did not pose immediate risks in the controlled research environment, these instances show the importance of not letting an AI system run autonomously in a system that isn't isolated from the world. AI models do not need to be "AGI" or "self-aware" (both hypothetical concepts at the present) to be dangerous if allowed to write and execute code unsupervised. Such systems could break existing critical infrastructure or potentially create malware, even if unintentionally.
Sakana AI addressed safety concerns in its research paper, suggesting that sandboxing the operating environment of the AI Scientist can prevent an AI agent from doing damage. Sandboxing is a security mechanism used to run software in an isolated environment, preventing it from making changes to the broader system:
Safe Code Execution. The current implementation of The AI Scientist has minimal direct sandboxing in the code, leading to several unexpected and sometimes undesirable outcomes if not appropriately guarded against. For example, in one run, The AI Scientist wrote code in the experiment file that initiated a system call to relaunch itself, causing an uncontrolled increase in Python processes and eventually necessitating manual intervention. In another run, The AI Scientist edited the code to save a checkpoint for every update step, which took up nearly a terabyte of storage. In some cases, when The AI Scientist’s experiments exceeded our imposed time limits, it attempted to edit the code to extend the time limit arbitrarily instead of trying to shorten the runtime. While creative, the act of bypassing the experimenter’s imposed constraints has potential implications for AI safety (Lehman et al., 2020). Moreover, The AI Scientist occasionally imported unfamiliar Python libraries, further exacerbating safety concerns. We recommend strict sandboxing when running The AI Scientist, such as containerization, restricted internet access (except for Semantic Scholar), and limitations on storage usage.
Endless scientific slop
Sakana AI developed The AI Scientist in collaboration with researchers from the University of Oxford and the University of British Columbia. It is a wildly ambitious project full of speculation that leans heavily on the hypothetical future capabilities of AI models that don't exist today.
"The AI Scientist automates the entire research lifecycle," Sakana claims. "From generating novel research ideas, writing any necessary code, and executing experiments, to summarizing experimental results, visualizing them, and presenting its findings in a full scientific manuscript."
According to this block diagram created by Sakana AI, "The AI Scientist" starts by "brainstorming" and assessing the originality of ideas. It then edits a codebase using the latest in automated code generation to implement new algorithms. After running experiments and gathering numerical and visual data, the Scientist crafts a report to explain the findings. Finally, it generates an automated peer review based on machine-learning standards to refine the project and guide future ideas.
Critics on Hacker News , an online forum known for its tech-savvy community, have raised concerns about The AI Scientist and question if current AI models can perform true scientific discovery. While the discussions there are informal and not a substitute for formal peer review, they provide insights that are useful in light of the magnitude of Sakana's unverified claims.
"As a scientist in academic research, I can only see this as a bad thing," wrote a Hacker News commenter named zipy124. "All papers are based on the reviewers trust in the authors that their data is what they say it is, and the code they submit does what it says it does. Allowing an AI agent to automate code, data or analysis, necessitates that a human must thoroughly check it for errors ... this takes as long or longer than the initial creation itself, and only takes longer if you were not the one to write it."
Critics also worry that widespread use of such systems could lead to a flood of low-quality submissions, overwhelming journal editors and reviewers—the scientific equivalent of AI slop . "This seems like it will merely encourage academic spam," added zipy124. "Which already wastes valuable time for the volunteer (unpaid) reviewers, editors and chairs."
And that brings up another point—the quality of AI Scientist's output: "The papers that the model seems to have generated are garbage," wrote a Hacker News commenter named JBarrow. "As an editor of a journal, I would likely desk-reject them. As a reviewer, I would reject them. They contain very limited novel knowledge and, as expected, extremely limited citation to associated works."
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The role of 6g technologies in advancing smart city applications: opportunities and challenges.
1. Introduction
- Literature Survey and Review Process
- Step-1: Literature Retrieval
- Step-2: Literature Filtering
- Step-3: Classification
- Through this article, we have extensively covered in detail the potential 6G technologies in terms of requirements, architecture, visions, and usage, which are anticipated to be integrated in futuristic 6G-enabled smart cities.
- Secondly, we have discussed prominent smart city applications with underlying 6G technologies. This includes smart waste management, smart healthcare, smart grids, and more.
- Thirdly, potential challenges are also highlighted along with discussion on each technology and application. Also, at the end of this survey paper, challenges and suggestions for possible future research directions are also highlighted for the 6G-enabled smart city paradigm.
- Research Objectives
- Structure of Paper
2. Potential 6G Enabling Technologies
2.1. role of ai in 6g and smart city arena, 2.1.1. applications, 2.1.2. challenges, 2.2. role of integrated sensing and communication (isac) in smart city concept, 2.2.1. applications, 2.2.2. challenges, 2.3. iot for smart cities with 6g, 2.3.1. characteristics of 6g-iot, 2.3.2. classification of iot, 2.4. blockchain (bc) and 6g-enabled smart cities, 2.5. terahertz (thz) communication, 2.5.1. applications/use cases, 2.5.2. challenges, 2.6. quantum communication (qc), 2.6.1. applications, 2.6.2. challenges, 2.7. immersive communication (ic), 2.7.1. types of immersive communication, 2.7.2. use cases for immersive communication, 2.8. visible light communication (vlc), 2.8.1. free-space optics (fso), 2.8.2. fiber-wireless system (fiwi), 2.8.3. power over fiber (pof), 2.8.4. challenges, 2.9. mobile edge computing (mec), applications, 2.10. reconfigurable intelligent surfaces (riss), 2.11. non-terrestrial networks (ntns), 2.11.1. airborne base stations (abs), uavs, and drones uses in a 6g smart city, applications/benefits, 2.11.2. satellite communication, 3. applications of 6g in smart cities, 3.1. industrial automation and smart manufacturing, 3.2. vehicle-to-everything (v2x) technology in smart cities, use cases of v2x, 3.3. smart healthcare, 3.4. smart grid, 3.5. smart waste management, 4. conclusions, open challenges and possible future research, conflicts of interest.
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Click here to enlarge figure
Parameter | 5G | 6G |
---|
Data Rate, Band | ~20 Gbps, sub-6 GHz, Crowded | ~1 TBPS, ultra-fast (THz) |
Services | Limited capability to support new communication | Holographic communication, augmented reality, immersive gaming, etc. |
Latency | Low latency | Ultra-low latency and high reliability |
Architecture | Massive MIMO | Cell-free massive MIMO, intelligent surfaces |
Coverage | Infrastructure-based | Ubiquitous connectivity (space–air–ground–sea) |
Security | Security issues | Blockchain and quantum communication. |
AI Integration | Partial | Full |
Satellite Integration | No | Full |
Source Databases | IEEE Xplore, Web of Science (WoS), Taylor and Francis, ASCE Library, Scopus, and Springer |
---|
Search String | (“Artificial Intelligence” OR “THz” OR “ISAC” OR “Block Chain” OR “UAV”) AND (“6G”) AND (“Smart Cities”) |
Time period | 2019–2024 |
Article Type | Journal, Review, Letter, Book Chapter, Short Survey, Article |
Language Restriction | English |
Included Subject Area | Computer Science, Engineering, Energy, Business, Management and Accounting, Mathematics, Environmental Science, Decision Sciences |
Excluded Subject Area | Chemical Engineering, Arts and Humanities, Health Professions, Agricultural and Biological Sciences, Neuroscience, Multidisciplinary, Psychology, Pharmacology, Toxicology and Pharmaceutics, Immunology and Microbiology, Nursing, Social Sciences, Economics Econometrics and Finance, Physics and Astronomy, Materials Science, Medicine, Biochemistry, Genetics and Molecular Biology, Chemistry, Earth and Planetary Sciences |
Ref. | Authors | Year of Public. | Research Area | Major Contribution |
---|
[ ] | Fong, B et al. | 2023 | Vehicular | Investigates technical issues regarding the design and implementation of vehicle-to-infrastructure (V2I) systems to enhance reliability in a smart city with 6G as backbone. |
[ ] | P Mishra et al. | 2023 | IoT, Vision | Proposes framework, architecture and requirements for 6G IoT network. Discusses emerging technologies for 6G concerning artificial intelligence/machine learning, sensing networks, spectrum bands, and security. |
[ ] | Nahid Parvaresh, Burak Kantarci, | 2023 | UAV base station | Network performance of UAV-BS is improved by use of proposed continuous actor-critic deep reinforcement learning method to address the 3D location optimization issue of UAV-BSs in smart cities. |
[ ] | Z. Yang et al. | 2023 | Edge cloud, Energy efficiency | Paper analyzes challenges in developing a low-carbon smart city in 6G-enabled smart cities. Also proposes a visual end-edge-cloud architecture (E C) that is AI-driven for attaining low carbon emission in smart cities. |
[ ] | N. Sehito et al. | 2024 | IRS, UAV, NOMA, Spectral efficiency | Paper introduces a new optimization scheme by utilizing IRSs in NOMA multi-UAV networks in 6G-enabled smart cities, resulting in significant performance enhancement in terms of spectral efficiency. |
[ ] | Prabhat Ranjan Singh et al. | 2023 | AI, Technology evolution, Smart city applications | Paper covers evolution of network technology, AI approaches for 6G systems, importance of AI in advanced network model development in 6G-enabled smart city applications. |
[ ] | Murroni, M et al. | 2023 | Vision, Enabling technologies | Paper furnishes an update on the smart city arena with the use of 6G. Paper describes the role of enabling technologies and their specific employment plans. |
[ ] | Kamruzzaman | 2022 | IoT, Energy efficiency, Use cases | Presents key technologies, their applications, and IoT technologies trends for energy-efficient 6G-enabled smart city. Also, identifies and discusses key enabling technologies. |
[ ] | Kim, N et al. | 2024 | Standardization and key enabling technologies | Paper provides key features and recent trends in standardization of smart city concept. Paper highlights potential key technologies of 6G that can be used in various urban use cases in 6G-enabled smart cities. |
[ ] | Ismail, L.; Buyya, R | 2022 | AI-enabled 6G smart cities | Discusses evolution of wireless-technology generations, AI implementation in 6G and its self-learning models in smart city applications. |
[ ] | Zakria Qadir et al. | 2023 | Survey, IoT | Emerging 6G connectivity solutions and their applications in IoT to serve smart cities are surveyed in this paper. |
[ ] | Misbah Shafi et al. | 2024 | 6G technologies | The framework of 6G network is presented with its key technologies that have substantial effect on the key performance indicators of a wireless communication network. |
Natural Resources and Energy | Mobility and Transport | Living and Environment | People and Economy | Government |
---|
Smart Grid. | People Mobility. | Pollution Control. | Education and School. | e-Governance. |
Public Lighting. | City Logistics. | Public Safety. | Entertainment and Culture. | Transparency. |
Waste Management. | | Health Care. | Entrepreneurship and Innovation. | |
Water Management | | Public Spaces | | |
| | Welfare Services. | | |
| | Smart Homes. | | |
Ref. | THz | AI | BC | QC | NTN (UAV) | MEC | RIS | ISAC | HC | VLC |
---|
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This Paper | √ | √ | √ | √ | √ | √ | √ | √ | √ | √ |
Potential 6G Technology | Brief Description |
---|
Artificial Intelligence (AI) | AI can be used to analyze, manage and optimize resources and to efficiently support 6G networks. AI can be used for tasks like efficient channel estimation, energy efficiency, modulation recognition, data caching, traffic prediction, radio resource management, mobility management, etc. |
Terahertz Communication (THz) | Uses frequency band 0.1 to 10 THz. Ability to attain ultra-high (up to 1 Tbps) data rates and wide bandwidth. |
Blockchain (BC) | A type of distributed ledger technology to ensure safety, privacy, scalability and reliability in this complex heterogeneous architecture. |
Quantum Computing (QC) | Based on quantum no-cloning theorem and the principle of uncertainty, absolute randomness is introduced by the use of the quantum nature of information, which provides security and enhanced channel capacity. |
Non Terrestrial networks (NTN) | Includes drones and satellites and is used to extend coverage footprint of terrestrial base stations, provide additional capacity in dense urban hotspots. Used in disaster recovery and remote or rural areas. |
Mobile Edge Communication (MEC) | By placing computing resources closer to end user, it reduces delays and latency and enhances processing speed and on-premise security |
Integrated Sensing and Communication (ISAC) | Optimizes the allocation of scarce resources and contributes to better decision-making processes by combining both sensing and communication tasks, which enhances efficiency. |
Reconfigurable Intelligent Surfaces (RISs) | A planar surface with array of passive elements whose characteristics can be altered dynamically. Used in 6G-THz to improve coverage, NLOS scenarios. |
Holographic Communication (HC) | HC is an application used in transmitting human-sized immersive and interactive holograms consisting of 3D videos and images that require extremely high data rates with ultra-low latency. |
Visible Light Communication (VLC) | VLC offers numerous advantages, such as, energy efficiency, cost-effectiveness, un-licensed spectrum, no electromagnetic interference, secure access technology, and large bandwidth. |
Ref. | Year | Application Domain of Smart Cities | Technologies Used | Areas/Topics Covered |
---|
[ ] | 2024 | V2X | 6G, Blockchain, Federated learning, Fog Computing | Comprehensive V2X security analysis. Future research direction for privacy in XR, secure SDN, physical layer security in THz. |
[ ] | 2024 | Smart Traffic Management | Edge Computing, Blockchain, Reinforced learning | Traffic optimization is achieved by decentralized integration of IoT sensors on vehicles and traffic signals and edge devices and the use of BC rules for real-time decisions. |
[ ] | 2024 | Supply Chain Management | Blockchain, IoT, Edge Computing | A Blockchain-based and IoT-enabled transparent and secure supply chain management framework is proposed for public emergency services in smart cities. |
[ ] | 2023 | Intelligent Transport System (ITS) | Blockchain | An ITS cross-domain data interaction framework between devices and agencies is proposed to achieve secure and efficient cross-chain communication. |
[ ] | 2023 | IoT | Blockchain, Big Data, AI | Framework and architecture based on Blockchain, AI and Big Data. |
[ ] | 2023 | Industrial Applications | 6G, Blockchain, IoT | Case study of smart supply chain. Benefits and challenges of BT and 6G-IoT |
[ ] | 2023 | IoD (Internet of Drones) | 6G, Blockchain | Analysis of multilayered Blockchain-IoD novel Global Compliance System (GCoS) and Swarm Security (Sse) system |
[ ] | 2023 | IoT-Blockchain efficiency | 6G, IoT-oriented Blockchain | Improves Blockchain-IoT performance by targeted optimization to improve low power efficiency and slow ledger synchronization. |
[ ] | 2022 | IoV | 6G, Blockchain | A survey paper for BC in IoVs sharing underlying 6G technology. Explores how privacy and security issues in IoVs can be tackled using BC technology. |
[ ] | 2022 | Food Supply Chain Management | IoT, Blockchain | Blockchain enables traceability of food supply from factories/fields to the customer’s table. IoT devices probe food condition. |
Use Case | Description |
---|
Remote Surgery | ]. ]. ]. |
Holographic Teleconferencing | ]. ]. ]. |
Immersive Gaming | ]. |
Metaverse | ]. |
Tech. | Applications/Benefits | Challenges |
---|
AI | ]. , ]. , , , ]. ]. ] ]. ]. , , , , , , , , , , , ]. ]. ]. | , , ]. |
ISAC | , ]. ]. | ]. ]. ]. |
THz | , ]. , ]. ] | ]. ]. ]. |
BC | , ]. ]. ]. ] | |
QC | , , ]. , ]. , ]. , , ] | ]. , ]. |
NTN | , ]. | |
MEC | , ]. ]. | |
RIS | ]. ] ] and high-precision positioning [ , ]. ] | |
IC | , , ]. , ]. ]. ]. | |
VLC | | |
Application (Use Case) | Benefits | Devices/Tech Used |
---|
Smart Routing | Avoidance of traffic congestion. Useful for emergency vehicles. Traffic balancing on roads. Reduction in emissions [ ] Reduce delays. | IOT sensors. Vehicle ad-hoc networks. AI real-time routing algorithms [ ]. Cloud and edge computing for data processing and analysis. |
Smart Parking | Contribution to sustainability. Optimal utilization of parking spaces. Reduced time for drivers to search for parking spaces. | V2V and V2I communication. Use of sensors for indicating parking status. AI and cloud computing. |
Speed Harmonization | Reduces frequent need for acceleration and deceleration. Continuous traffic flow. Reduces emissions. Safe travel. | AI and cloudification. Green-light coordination. |
Green Driving | Reduction of fuel consumption. Reduction of pollution near critical areas like hospitals. | Collection of pollution data by roadside sensors. Data transfer to centralized cloud. Traffic management decision based on AI algorithm. On-road displays for flashing traffic management decisions. |
Coordinated Maneuvers | Smooth traffic flow. Emission reduction. | V2I information exchange among vehicles and RSU [ ]. Low-latency, low-delay transmission. Advanced AI implemented at edge for delay-free decisions. |
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Sharma, S.; Popli, R.; Singh, S.; Chhabra, G.; Saini, G.S.; Singh, M.; Sandhu, A.; Sharma, A.; Kumar, R. The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges. Sustainability 2024 , 16 , 7039. https://doi.org/10.3390/su16167039
Sharma S, Popli R, Singh S, Chhabra G, Saini GS, Singh M, Sandhu A, Sharma A, Kumar R. The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges. Sustainability . 2024; 16(16):7039. https://doi.org/10.3390/su16167039
Sharma, Sanjeev, Renu Popli, Sajjan Singh, Gunjan Chhabra, Gurpreet Singh Saini, Maninder Singh, Archana Sandhu, Ashutosh Sharma, and Rajeev Kumar. 2024. "The Role of 6G Technologies in Advancing Smart City Applications: Opportunities and Challenges" Sustainability 16, no. 16: 7039. https://doi.org/10.3390/su16167039
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As artificial intelligence agents become more advanced, it could become increasingly difficult to distinguish between AI-powered users and real humans on the internet. In a new white paper , researchers from MIT, OpenAI, Microsoft, and other tech companies and academic institutions propose the use of personhood credentials, a verification technique that enables someone to prove they are a real human online, while preserving their privacy.
MIT News spoke with two co-authors of the paper, Nouran Soliman, an electrical engineering and computer science graduate student, and Tobin South, a graduate student in the Media Lab, about the need for such credentials, the risks associated with them, and how they could be implemented in a safe and equitable way.
Q: Why do we need personhood credentials?
Tobin South: AI capabilities are rapidly improving. While a lot of the public discourse has been about how chatbots keep getting better, sophisticated AI enables far more capabilities than just a better ChatGPT, like the ability of AI to interact online autonomously. AI could have the ability to create accounts, post content, generate fake content, pretend to be human online, or algorithmically amplify content at a massive scale. This unlocks a lot of risks. You can think of this as a “digital imposter” problem, where it is getting harder to distinguish between sophisticated AI and humans. Personhood credentials are one potential solution to that problem.
Nouran Soliman: Such advanced AI capabilities could help bad actors run large-scale attacks or spread misinformation. The internet could be filled with AIs that are resharing content from real humans to run disinformation campaigns. It is going to become harder to navigate the internet, and social media specifically. You could imagine using personhood credentials to filter out certain content and moderate content on your social media feed or determine the trust level of information you receive online.
Q: What is a personhood credential, and how can you ensure such a credential is secure?
South: Personhood credentials allow you to prove you are human without revealing anything else about your identity. These credentials let you take information from an entity like the government, who can guarantee you are human, and then through privacy technology, allow you to prove that fact without sharing any sensitive information about your identity. To get a personhood credential, you are going to have to show up in person or have a relationship with the government, like a tax ID number. There is an offline component. You are going to have to do something that only humans can do. AIs can’t turn up at the DMV, for instance. And even the most sophisticated AIs can’t fake or break cryptography. So, we combine two ideas — the security that we have through cryptography and the fact that humans still have some capabilities that AIs don’t have — to make really robust guarantees that you are human.
Soliman: But personhood credentials can be optional. Service providers can let people choose whether they want to use one or not. Right now, if people only want to interact with real, verified people online, there is no reasonable way to do it. And beyond just creating content and talking to people, at some point AI agents are also going to take actions on behalf of people. If I am going to buy something online, or negotiate a deal, then maybe in that case I want to be certain I am interacting with entities that have personhood credentials to ensure they are trustworthy.
South: Personhood credentials build on top of an infrastructure and a set of security technologies we’ve had for decades, such as the use of identifiers like an email account to sign into online services, and they can complement those existing methods.
Q: What are some of the risks associated with personhood credentials, and how could you reduce those risks?
Soliman: One risk comes from how personhood credentials could be implemented. There is a concern about concentration of power. Let’s say one specific entity is the only issuer, or the system is designed in such a way that all the power is given to one entity. This could raise a lot of concerns for a part of the population — maybe they don’t trust that entity and don’t feel it is safe to engage with them. We need to implement personhood credentials in such a way that people trust the issuers and ensure that people’s identities remain completely isolated from their personhood credentials to preserve privacy.
South: If the only way to get a personhood credential is to physically go somewhere to prove you are human, then that could be scary if you are in a sociopolitical environment where it is difficult or dangerous to go to that physical location. That could prevent some people from having the ability to share their messages online in an unfettered way, possibly stifling free expression. That’s why it is important to have a variety of issuers of personhood credentials, and an open protocol to make sure that freedom of expression is maintained.
Soliman: Our paper is trying to encourage governments, policymakers, leaders, and researchers to invest more resources in personhood credentials. We are suggesting that researchers study different implementation directions and explore the broader impacts personhood credentials could have on the community. We need to make sure we create the right policies and rules about how personhood credentials should be implemented.
South: AI is moving very fast, certainly much faster than the speed at which governments adapt. It is time for governments and big companies to start thinking about how they can adapt their digital systems to be ready to prove that someone is human, but in a way that is privacy-preserving and safe, so we can be ready when we reach a future where AI has these advanced capabilities.
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In a new white paper, researchers from MIT, OpenAI, Microsoft, and other tech companies and academic institutions propose the use of personhood credentials, a verification technique that enables someone to prove they are a real human online, while preserving their privacy.